Machine learning is an exciting and fast-moving field at
the intersection of computer science, statistics, and
optimization with many recent consumer
applications (e.g., Microsoft Kinect, Google Translate,
Iphone's Siri, digital camera face detection, Netflix
recommendations, Google news). Machine learning and
computational statistics also play a central role in data
science. In this graduate-level class, students will learn
about the theoretical foundations of machine learning and
computational statistics and how to apply these to solve
new problems. This is a required course for the MS in Data
Science and should be taken in the first year of study; it
is also suitable for MS and Ph.D. students in Computer
Science and related fields (see pre-requisites below).

Problem
Set policy
I expect you to try solving each problem set on your own.
However, when being stuck on a problem, I encourage
you to collaborate with other students in the class, subject
to the following rules:

You may discuss a problem with any student in this
class, and work together on solving it. This can involve
brainstorming and verbally discussing the problem, going
together through possible solutions, but should not
involve one student telling another a complete solution.

Once you solve the homework, you must write up
your solutions on your own, without looking at
other people's write-ups or giving your write-up to
others.

In your solution for each problem, you must write
down the names of any person with whom you
discussed it. This will not affect your grade.

Do not consult solution manuals or other people's
solutions from similar courses.

Late submission policy: During the
semester you are allowed at most two extensions on the
homework assignment. Each extension is for at most 48 hours
and carries a penalty of 25% off your assignment.